This work proposes to develop, analyze and apply a spatially-explicit mathematical model for the tick-borne disease, human monocytic ehrlichiosis (causitive agent: Ehrlichia chaffeensis sp.). The project is part of the mentored career development plan for Holly D. Gaff with J. Stephen Dumler as primary mentor. Through the course of this mentored project and the completion of a Master in Public Health, she will have a solid foundation for a successful career making significant contributions to the study of infectious diseases and to the betterment of public health. Dr. Gaff's long-term career goal is to help establish a lab of students, postdoctoral fellows, other faculty and staff to explore the ecology of infectious diseases with specific focus on tick-borne and other vector-borne diseases. The project will explore a variety of mathematical models to gain insights into ehrlichiosis and to identify the strongest model for each population involved. The identified primary vector of HME is the Lone Star tick (Amblyomma americanum), and the primary reservoir host is the white-tailed deer (Odocoileus virginianus). The starting hypothesis is the model system this will involve an age-structured, stochastic model for the tick populations and an individual-based model for the host populations. This model will then be used to test hypotheses regarding the importance of various tick and host species as well as impacts of weather and habitat variability on the spread and control of HME. Finally, various mitigation strategies will be analyzed using optimal control techniques in the hopes of finding scenarios that will minimize the risk of HME to humans. Relevance: The results of this modeling effort will be new insights into the spatial and temporal distribution of human tick-borne disease outbreaks. Insights into the distribution patterns can be helpful for notification of medical professionals to be watchful for influx of patients with tick-borne diseases, which otherwise may be misdiagnosed. Additionally, the analysis of the model will provide guidance for more effective treatment strategies to hopefully reduce the numbers of HME cases. [unreadable] [unreadable] [unreadable] [unreadable]